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From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Paul De Vos, Bernard De Baets
2010 BMC Bioinformatics  
Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species.  ...  Results: In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm.  ...  W.W. is supported by a postdoctoral grant from the Research Foundation of Flanders. The authors thank the anonymous reviewers for helpful comments and suggestions.  ... 
doi:10.1186/1471-2105-11-69 pmid:20113515 pmcid:PMC2828439 fatcat:awynynliwjejjgpxoezmbpejzq

Hierarchical Metric Learning for Optical Remote Sensing Scene Categorization [article]

Akashdeep Goel, Biplab Banerjee, Aleksandra Pizurica
2018 arXiv   pre-print
We address the problem of scene classification from optical remote sensing (RS) images based on the paradigm of hierarchical metric learning.  ...  As a remedy, we propose to organize the classes in a hierarchical fashion by exploring their visual similarities and subsequently learn separate distance metric transformations for the classes present  ...  Building a hierarchical binary tree structure of the visual categories using maximum-margin clustering The goal of this stage is to organize the RS scene classes in a hierarchical binary tree fashion by  ... 
arXiv:1708.01494v3 fatcat:7l43aeyfgzhlhcbpmmtwnf5yky

A data-driven clustering recommendation method for single-cell RNA-sequencing data

Yu Tian, Ruiqing Zheng, Zhenlan Liang, Suning Li, Fang-Xiang Wu, Min Li
2021 Tsinghua Science and Technology  
Finally, a data-driven clustering recommendation method, called DDCR, is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data.  ...  Among all the methods, hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning  ...  .: A Data-Driven Clustering Recommendation Method for Single-Cell RNA-Sequencing Data 779 Fig. 4 Visualizations of cells containing specific functional subsets based on t-SNE (a) and UMAP (b).  ... 
doi:10.26599/tst.2020.9010028 fatcat:lwcigi3pdbfenaoi2to3x3buy4

Detection of Visual Concepts and Annotation of Images Using Ensembles of Trees for Hierarchical Multi-Label Classification [chapter]

Ivica Dimitrovski, Dragi Kocev, Suzana Loskovska, Sašo Džeroski
2010 Lecture Notes in Computer Science  
In this paper, we present a hierarchical multi-label classification system for visual concepts detection and image annotation.  ...  To this end, we use predictive clustering trees (PCTs), which are able to classify target concepts that are organized in a hierarchy.  ...  Most of the systems for detection of visual concepts learn a separate model for each visual concept [7] .  ... 
doi:10.1007/978-3-642-17711-8_16 fatcat:vnieg4d4indkdgj4hosxrxyhna

Unseen Activity Recognitions: A Hierarchical Active Transfer Learning Approach

Mohammad Arif Ul Alam, Nirmalya Roy
2017 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)  
We first (a) design a hierarchical representation of complex activity taxonomy in terms of human-readable semantic attributes, and (b) develop a hierarchy of classifiers which incorporates a cluster tree  ...  To tackle this challenge, we extend Hierarchical Active Transfer Learning (HATL) approach that exploits semantic attribute cluster structure of complex activities shared between seen (source) and unseen  ...  Additionally we build a semantic attribute representation based hierarchical cluster tree pattern of complex activities and incorporated hierarchical sampling for active learning combining with transfer  ... 
doi:10.1109/icdcs.2017.264 dblp:conf/icdcs/AlamR17 fatcat:pyt5cey2ofahtcnmvecuughkqe

Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning [article]

Jared Markowitz, Aurora C. Schmidt, Philippe M. Burlina, I-Jeng Wang
2017 arXiv   pre-print
We address zero-shot (ZS) learning, building upon prior work in hierarchical classification by combining it with approaches based on semantic attribute estimation.  ...  Using a dataset consisting of 150 object classes from the ImageNet ILSVRC2012 data set, we find that the hierarchical classification method that maximizes expected reward for non-novel classes differs  ...  Acknowledgments We thank the authors of [ ] for providing the class to attribute mapping of their data set.  ... 
arXiv:1712.03151v1 fatcat:maz4josz7baehms6ixbscowf2e

Hierarchical PCA Using Tree-SOM for the Identification of Bacteria [chapter]

Stephan Simmuteit, Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa
2009 Lecture Notes in Computer Science  
A modified variant of the Evolving Tree is developed and applied to obtain a hierarchical clustering.  ...  In this paper we present an extended version of Evolving Trees using Oja's rule. Evolving Trees are extensions of Self-Organizing Maps developed for hierarchical classification systems.  ...  With increasing depth of the tree the data are clustered by the Tree-SOM approach and a hierarchical PCA analysis of the sub-clusters become available.  ... 
doi:10.1007/978-3-642-02397-2_31 fatcat:sczzptoidjbgdixo6zrwz5pqiu

Implementing Hierarchical Indoor Semantic Location Identity Classification: A Case Study for COVID-19 Proximity Tracking in the Philippines [chapter]

Irvin Kean Paulus Paderes, Ligayah Leah Figueroa, Rommel Feria
2021 Frontiers in Artificial Intelligence and Applications  
There is also a novel method of classification framework, called hierarchical classification, that leverages the hierarchical structure of the labels to reduce model complexity.  ...  handling of geospatial data by implementing a hybrid hierarchical indoor semantic location identity classification that focuses on observable events within context-unique locations.  ...  . / Hierarchical Indoor Semantic Location Identity Classification  ... 
doi:10.3233/faia210087 fatcat:fft454ikyzgxtal7xq6ng5r74i

Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization [article]

Fei Ding, Xiaohong Zhang, Justin Sybrandt, Ilya Safro
2020 arXiv   pre-print
In addition, our visualization of hierarchical representations indicates that our method can capture meaningful and interpretable clusters.  ...  Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification.  ...  We feed the original graph to output a coarser one based on the learned hierarchical cluster assignments.  ... 
arXiv:2003.08420v3 fatcat:n2u3becfmzdkfmyei7q3qm5xoi

Deep hierarchical embedding for simultaneous modeling of GPCR proteins in a unified metric space

Taeheon Lee, Sangseon Lee, Minji Kang, Sun Kim
2021 Scientific Reports  
Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset.  ...  In this study, we propose DeepHier, a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model.  ...  Distance matrices in Fig. 3 were drawn with Seaborn 0.11, a python library for visualizing statistical data 43 .  ... 
doi:10.1038/s41598-021-88623-8 pmid:33953216 fatcat:cxewfy4ofje5nermpfr6ndmvn4

Learning Action Concept Trees and Semantic Alignment Networks from Image-Description Data [article]

Jiyang Gao, Ram Nevatia
2016 arXiv   pre-print
We propose a method to learn an Action Concept Tree (ACT) and an Action Semantic Alignment (ASA) model for classification from image-description data via a two-stage learning process.  ...  Action classification in still images has been a popular research topic in computer vision.  ...  We would like to thank Chen Sun for valuable discussions.  ... 
arXiv:1609.02284v1 fatcat:6me3oqfsjjek3fzcngcxszosya

Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework [article]

Shu Zhang and Ran Xu and Caiming Xiong and Chetan Ramaiah
2022 arXiv   pre-print
In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes.  ...  Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks.  ...  See Figure 1 (b) for a sample representation in a tree structure.  ... 
arXiv:2204.13207v1 fatcat:zux6lp24drbbhbbzoflhomhwyi

Vocabulary hierarchy optimization for effective and transferable retrieval

Rongrong Ji, Xing Xie, Hongxun Yao, Wei-Ying Ma
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
We deployed a large-scale image retrieval system using a vocabulary tree model to validate our advances.  ...  We adopt a novel Density-based Metric Learning (DML) algorithm, which corrects word quantization bias without supervision in hierarchy optimization, based on which we present a hierarchical rejection chain  ...  In particular, we first introduce a Density-based Metric Learning (DML) to unsupervisedly refine the similarity metric in the hierarchical clustering.  ... 
doi:10.1109/cvpr.2009.5206680 dblp:conf/cvpr/JiXYM09 fatcat:otadc56kobh2dhxsvm3efrimfi

An Efficient Indexing for Content Based Image Retrieval Based on Number of Clusters Using Clustering Technique

Monika Jain, S. K. Singh, Kavita Saxena
2017 International Journal of Artificial Intelligence and Applications for Smart Devices  
This paper focuses on a cluster based indexing technique for achieving efficient and effective retrieval performance.  ...  A new cluster based similarity measure conforming to human perception is applied and shown to be effective. An unsupervised learning technique has been used to find number of clusters.  ...  Common clustering algorithms include:  Hierarchical clustering: creates a cluster tree by building multilevel hierarchy of objects.  k-Means clustering: partitions data into k distinct clusters based  ... 
doi:10.14257/ijaiasd.2017.5.1.01 fatcat:ooekqrtzmzeb7bculblaa4axuu

Applying Educational Data Mining to Explore Students' Learning Patterns in the Flipped Learning Approach for Coding Education

Hui-Chun Hung, I-Fan Liu, Che-Tien Liang, Yu-Sheng Su
2020 Symmetry  
By using the hierarchical clustering heat map, this study could define the students' learning patterns including the positive interactive group, stable learning group, positive teaching material group,  ...  The experimental data were collected from two classes of Python programming related courses for first-year students in a university in northern Taiwan.  ...  Moreover, to have a closer understanding of the tree structure, the tree diagram was added to the group heat map to generate a hierarchical clustering heat map, as shown in Figure 1 .  ... 
doi:10.3390/sym12020213 fatcat:ppewbeckevc6ljxqjotuohvne4
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